Preoperative Patient Factors that Predict
Outcome After Total Knee Replacement
A Systematic Review
MR Dunbar, DR Griffin, G Surr
Warwick Medical School
Potentially Influential Factors
Patient factors
Institution factors
Staff
Environment
Implant factors
Manufacturers believe so
Surgeon/operative factors
Experience
“Psychotropic surgeon”
Potentially Influential Factors
Patient factors
Institution factors
Staff
Environment
Implant factors
Manufacturers believe so
Surgeon/operative factors
Experience
“Psychotropic surgeon”
Total Knee Replacement
30,000/yr in England
Potentially > 55,000 patients eligible
Many decline surgery
Poor understanding of risk vs benefit
Jüni et al, 2003
Question
In patients with arthritis of the knee, who
undergo a primary total knee
replacement, which patient factors are
associated with better functional and
patient-derived outcomes?
Question
In patients with arthritis of the knee,
who undergo a primary total knee
replacement, which patient factors are
associated with better functional and
patient-derived outcomes?
Question
In patients with arthritis of the knee, who
undergo a primary total knee
replacement, which patient factors are
associated with better functional and
patient-derived outcomes?
Question
In patients with arthritis of the knee, who
undergo a primary total knee
replacement, which patient factors are
associated with better functional and
patient-derived outcomes?
Patient Factors
Age
Gender
BMI
Medical co-morbidity
Physical function
General and mental health
Measurement Tools Criteria
Valid
Responsive
Reliable
Outcome Measures
Western Ontario MacMaster Osteoarthritis Index
(WOMAC)
Knee injury and Osteoarthritis Outcomes Score
(KOOS)
Medical Outcomes Study Short Form 36 (SF-36)
American Knee Score (AKS or KSS)
Oxford Knee Score (OKS)
SR Methodology
Search strategy
Quality scoring
Data extraction
Analysis
Discussion
Search Results
Study
Year
Study
Design
No of
TKRs
Minimum
follow-up
Outcome
measures
Analysis (simplified)
Sharma et al
1996
Cohort
52
3 months
SF-36
Hierarchical multiple regression
Konig et al
1997
Cohort
276
2 yrs
AKS
Multiple linear regression
Heck et al
1998
Cohort
330
2 yrs
WOMAC,
SF-36
Logistic regression
Wasielewski et al
1998
Cohort
106
3 months
AKS
ANOVA
Hawker et al
1998
Crosssectional
survey
1193
2 yrs
SF-36,
WOMAC
Stepwise multiple linear regression
Fortin et al
1999
Cohort
106
6 months
SF-36,
WOMAC
Multiple linear regression
Spicer et al
2001
Casecontrol
751
4 yrs
AKS
Wilcoxon Rank sum, KruskalWallis
Allyson Jones et
al
2003
Cohort
294
6 months
SF-36,
WOMAC
Stepwise multiple linear regression
Brander et al
2003
Cohort
149
1 yr
AKS
Multiple regression
Lingard et al
2004
Cohort
860
1 yr
SF-36,
WOMAC
Hierarchical multiple regression
Quality Scoring
Selection
(max )
Compatibility
(max )
Exposure
(max )
Heck et al
-
Sharma et al
-
Konig et al
-
Spicer et al
Fortin et al
-
Wasielewski et al
-
Lingard et al
Allyson Jones et al
-
Brander et al
-
Study
Regression Analysis
Outcome = b1X1 + b2X2 + b3X3 + ….. + _
Regression Analysis
Outcome = b1(preop function) + b2(age) + b3(BMI)+ _
Regression Analysis
Outcome = b1(preop function) + b2(age) + b3(BMI)+ _
Regression coefficient
or parameter estimate
Regression Analysis
Outcome = b1(preop function) + _
Outcome
Predictor (preop function)
Regression Analysis
Outcome = b1(preop function) + _
Outcome
x
x
x
xx
x
x
x
x
x x
x
Predictor (preop function)
Regression Analysis
Outcome = b1(preop function) + _
Outcome
x
x
x
xx
x
x
x
x
x x
x
Predictor (preop function)
Regression Analysis
Outcome = b1(preop function) + _
Outcome
Slope = b1
Predictor (preop function)
Regression Analysis
Outcome = b1(preop function) + _
Outcome
Slope = b1
Mrs Jones
Predictor (preop function)
Regression Analysis
Outcome = b1(preop function) + _
Outcome
Slope = b1
Mrs Jones
Predictor (preop function)
Results – Strongest Predictors
Predictor
Outcome
Parameter Estimate
WOMAC
function
WOMAC function
SF-36 PF
0.3-0.6
0.1
WOMAC pain
WOMAC pain
0.2-0.4
SF-36 PF
SF-36 MH, MCS
SF-36 PF
0.3
0.1
Co-morbidity
SF-36 PF
WOMAC function
WOMAC pain
-4.1
-2.2
-1.2
Non-predictors
Age
Gender
BMI
Discussion
Score predicting score
Study size
Small parameter estimates
Best model 36% variance
Future Research
Larger studies
Other factors
Other outcomes
Trials to determine effect of
modifying patient factors
Warwick
Medical School
University Hospitals
of Coventry and
Warwickshire